Cross-lingual Linking of Automatically Constructed Frames and FrameNet (2022.lrec-1)
Copied to clipboard
| Challenge: | Existing semantic frame resources have been manually elaborated, but manual development is labor-intensive. |
| Approach: | They propose to link Japanese frames to English FrameNet by using cross-lingual word embeddings and a model that takes only the frame-evoking words into account. |
| Outcome: | The proposed model will facilitate the development of cross-lingual frame resources. |
Similar Papers
Frame Semantics across Languages: Towards a Multilingual FrameNet (C18-3)
Copied to clipboard
| Challenge: | This workshop will present current research on aligning Frame Semantic resources across languages . resources based on FrameNet have been created for roughly a dozen languages based upon Fillmore's Frame Sementics . |
| Approach: | This workshop will present current research on aligning Frame Semantic resources across languages . resources based on FrameNet have been created for roughly a dozen languages based upon Fillmore's Frame Sementics . |
| Outcome: | This workshop will present current research on aligning Frame Semantic resources across languages and automatic frame semantic parsing in English and other languages. |
Transfer of Frames from English FrameNet to Construct Chinese FrameNet: A Bilingual Corpus-Based Approach (L18-1)
Copied to clipboard
| Challenge: | Current publicly available Chinese FrameNet has a relatively low coverage of frames and lexical units compared with other languages. |
| Approach: | They propose an automatic way to construct Chinese FrameNet using a sentence-aligned English-Chinese bilingual corpus. |
| Outcome: | The proposed resource can provide frame recommendations acceptable by annotators. |
Definition Generation for Automatically Induced Semantic Frame (2024.findings-acl)
Copied to clipboard
| Challenge: | Semantic frames are conceptual structures that describe specific types of situations or events. |
| Approach: | They propose to generate frame definitions from a set of frame-evoking words using a large language model. |
| Outcome: | The proposed task incorporates frame element reasoning as chain-of-thought to enhance the inclusion of correct frame elements in the generated definitions. |
Crowdsourcing in the Development of a Multilingual FrameNet: A Case Study of Korean FrameNet (2020.lrec-1)
Copied to clipboard
| Challenge: | Using current methods, the construction of multilingual FrameNets is expensive and complex. |
| Approach: | They evaluated whether crowdsourcing approaches captured cross-cultural and cross-linguistic meanings . they found that crowd workers made intuitive choices comparable to trained FrameNet experts . |
| Outcome: | The results are now available in Korean FrameNet 1.1. |
Cross-lingual Structure Transfer for Relation and Event Extraction (D19-1)
Copied to clipboard
| Challenge: | Existing approaches to identify complex semantic structures are difficult to train from under-annotated sources. |
| Approach: | They exploit relation- and event-relevant language-universal features to train relation or event extractors from source annotations and apply them to target languages. |
| Outcome: | The proposed approach achieves comparable performance to state-of-the-art models trained on 3,000 manually annotated mentions. |
A Danish FrameNet Lexicon and an Annotated Corpus Used for Training and Evaluating a Semantic Frame Classifier (L18-1)
Copied to clipboard
| Challenge: | a Danish FrameNet is a lexicon based on the Danish Thesaurus . it is significantly faster than building a new one from scratch . |
| Approach: | They propose a way to efficiently compile a Danish FrameNet based on the Danish Thesaurus . they present the corresponding corpus annotations of frames and roles and show how this can be used for a semantic frame classifier . |
| Outcome: | The proposed approach is faster than building a lexicon from scratch. |
NutFrame: Frame-based Conceptual Structure Induction with LLMs (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing studies focus on syntactic knowledge and world knowledge, but conceptual structure is not well-understood. |
| Approach: | They propose a benchmark for coNceptual structure induction based on FrameNet . they use prompts to induce conceptual structure of Framenet with LLMs . |
| Outcome: | The proposed model is able to induce conceptual structure of FrameNet with LLMs. |
Cross-lingual Structure Transfer for Zero-resource Event Extraction (2020.lrec-1)
Copied to clipboard
| Challenge: | Existing approaches for information extraction only use name tagging . Currently, most successful cross-lingual transfer learning methods are limited to sequence labeling . |
| Approach: | They propose a share-and-transfer framework to transfer graph structures across languages . they propose to convert sentences in any language to language-universal graph structures . |
| Outcome: | The proposed framework performs comparable to state-of-the-art models on three languages without annotations. |
Introducing Frege to Fillmore: A FrameNet Dataset that Captures both Sense and Reference (2022.lrec-1)
Copied to clipboard
| Challenge: | a widely supported claim in the fields of semantics and philosophy is that meaning arises from the combination of sense and reference. |
| Approach: | They propose a tool that facilitates both referential- and frame annotations of language-independent corpora. |
| Outcome: | The Dutch FrameNet annotation tool facilitates both referential- and frame annotations of language-independent corpora. |
A Crowdsourced Frame Disambiguation Corpus with Ambiguity (N19-1)
Copied to clipboard
| Challenge: | Using crowdsourcing, we have found that inter-annotator disagreement is at least partly caused by ambiguity inherent to the text and frames. |
| Approach: | They propose a crowdsourcing approach to capture inter-annotator disagreement by a list of frames with disagreement-based scores that express the confidence with which each frame applies to the word. |
| Outcome: | The proposed approach captures disagreement between the annotations of 1,000 word-sentence pairs and scores on the likelihood that each frame applies to the word. |